"""simple docstring"""



import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
   if is_torch_available():
      import torch
   if is_tf_available():
      import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


SCREAMING_SNAKE_CASE__:Optional[int]									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Dict									=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Dict									=			{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__:List[Any]									=			{
    """Salesforce/codegen-350M-mono""": 2048,
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Dict							  =						VOCAB_FILES_NAMES
 _snake_case       :					str							  =						PRETRAINED_VOCAB_FILES_MAP
 _snake_case       :					List[str]							  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case       :					Union[str, Any]							  =						["""input_ids""", """attention_mask"""]
 _snake_case       :					Tuple							  =						CodeGenTokenizer


 def __init__( self    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase="<|endoftext|>"    ,		lowerCamelCase="<|endoftext|>"    ,		lowerCamelCase="<|endoftext|>"    ,		lowerCamelCase=False    ,		**lowerCamelCase    ,		):
        super().__init__(
            lowerCamelCase    ,		lowerCamelCase    ,		tokenizer_file=lowerCamelCase    ,		unk_token=lowerCamelCase    ,		bos_token=lowerCamelCase    ,		eos_token=lowerCamelCase    ,		add_prefix_space=lowerCamelCase    ,		**lowerCamelCase    ,		)

        if kwargs.pop("add_bos_token"    ,		lowerCamelCase						):
               __a       =    kwargs.pop("name_or_path"    ,		""						)
               raise ValueError(
                   "Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
                   "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
                   F"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
                   F"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
                   "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
                   " so that the fast tokenizer works correctly."						)

        __a       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("add_prefix_space"    ,		lowerCamelCase						) != add_prefix_space:
               __a       =    getattr(lowerCamelCase    ,		pre_tok_state.pop("type"						)						)
               __a       =    add_prefix_space
               __a       =    pre_tok_class(**lowerCamelCase						)

        __a       =    add_prefix_space


 def 						a__   ( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    kwargs.get("is_split_into_words"    ,		lowerCamelCase						)
        assert self.add_prefix_space or not is_split_into_words, (
            F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._batch_encode_plus(*lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    kwargs.get("is_split_into_words"    ,		lowerCamelCase						)

        assert self.add_prefix_space or not is_split_into_words, (
            F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
            "to use it with pretokenized inputs."
        )

        return super()._encode_plus(*lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        __a       =    self._tokenizer.model.save(lowerCamelCase    ,		name=lowerCamelCase						)
        return tuple(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    super().decode(
            token_ids=lowerCamelCase    ,		skip_special_tokens=lowerCamelCase    ,		clean_up_tokenization_spaces=lowerCamelCase    ,		**lowerCamelCase    ,		)

        if truncate_before_pattern is not None and len(lowerCamelCase						) > 0:
               __a       =    self.truncate(lowerCamelCase    ,		lowerCamelCase						)

        return decoded_text






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        def find_re(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               __a       =    pattern.search(lowerCamelCase    ,		lowerCamelCase						)
               return m.start() if m else -1

        __a       =    [re.compile(lowerCamelCase    ,		re.MULTILINE						) for pattern in truncate_before_pattern]

        __a       =    list(re.finditer("^print"    ,		lowerCamelCase    ,		re.MULTILINE						)						)

        if len(lowerCamelCase						) > 1:
               __a       =    completion[: prints[1].start()]

        __a       =    list(re.finditer("^def"    ,		lowerCamelCase    ,		re.MULTILINE						)						)

        if len(lowerCamelCase						) > 1:
               __a       =    completion[: defs[1].start()]

        __a       =    0

        __a       =    [
            pos for pos in [find_re(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						) for terminal in terminals] if pos != -1
        ]

        if len(lowerCamelCase						) > 0:
               return completion[: min(lowerCamelCase						)]
        else:
               return completion

"""simple docstring"""



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    nn.ModuleList(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = True    ,		):
        for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase    ,		lowerCamelCase    ,		self.nets						)						):
               __a ,   __a       =    controlnet(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		)

               # merge samples
               if i == 0:
                      __a ,   __a       =    down_samples, mid_sample
               else:
                      __a       =    [
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(lowerCamelCase    ,		lowerCamelCase						)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		):
        __a       =    0
        __a       =    save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   lowerCamelCase    ,		is_main_process=lowerCamelCase    ,		save_function=lowerCamelCase    ,		safe_serialization=lowerCamelCase    ,		variant=lowerCamelCase    ,		)

               idx += 1
               __a       =    model_path_to_save + F"_{idx}"






 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    0
        __a       =    []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        __a       =    pretrained_model_path
        while os.path.isdir(lowerCamelCase						):
               __a       =    ControlNetModel.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               controlnets.append(lowerCamelCase						)

               idx += 1
               __a       =    pretrained_model_path + F"_{idx}"

        logger.info(F"{len(lowerCamelCase						)} controlnets loaded from {pretrained_model_path}."						)

        if len(lowerCamelCase						) == 0:
               raise ValueError(
                   F"No ControlNets found under {os.path.dirname(lowerCamelCase						)}. Expected at least {pretrained_model_path + '_0'}."						)

        return cls(lowerCamelCase						)
